Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations200000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory126.2 MiB
Average record size in memory661.9 B

Variable types

Text3
Numeric7
Categorical4
DateTime2

Alerts

city is highly overall correlated with dropoff_lat and 3 other fieldsHigh correlation
dropoff_lat is highly overall correlated with city and 1 other fieldsHigh correlation
dropoff_lng is highly overall correlated with city and 1 other fieldsHigh correlation
pickup_lat is highly overall correlated with city and 1 other fieldsHigh correlation
pickup_lng is highly overall correlated with city and 1 other fieldsHigh correlation
trip_id has unique values Unique
tip has 131307 (65.7%) zeros Zeros

Reproduction

Analysis started2025-05-20 18:54:42.519358
Analysis finished2025-05-20 18:56:34.250888
Duration1 minute and 51.73 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

trip_id
Text

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
2025-05-20T19:56:35.195615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1400000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st rowT000000
2nd rowT000001
3rd rowT000002
4th rowT000003
5th rowT000004
ValueCountFrequency (%)
t199984 1
 
< 0.1%
t199985 1
 
< 0.1%
t199986 1
 
< 0.1%
t199987 1
 
< 0.1%
t199988 1
 
< 0.1%
t199989 1
 
< 0.1%
t199990 1
 
< 0.1%
t199991 1
 
< 0.1%
t199992 1
 
< 0.1%
t199993 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2025-05-20T19:56:36.302173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 200000
14.3%
1 200000
14.3%
0 200000
14.3%
9 100000
7.1%
8 100000
7.1%
3 100000
7.1%
2 100000
7.1%
7 100000
7.1%
6 100000
7.1%
5 100000
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 200000
14.3%
1 200000
14.3%
0 200000
14.3%
9 100000
7.1%
8 100000
7.1%
3 100000
7.1%
2 100000
7.1%
7 100000
7.1%
6 100000
7.1%
5 100000
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 200000
14.3%
1 200000
14.3%
0 200000
14.3%
9 100000
7.1%
8 100000
7.1%
3 100000
7.1%
2 100000
7.1%
7 100000
7.1%
6 100000
7.1%
5 100000
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 200000
14.3%
1 200000
14.3%
0 200000
14.3%
9 100000
7.1%
8 100000
7.1%
3 100000
7.1%
2 100000
7.1%
7 100000
7.1%
6 100000
7.1%
5 100000
7.1%
Distinct10000
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
2025-05-20T19:56:37.155151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1200000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR05207
2nd rowR09453
3rd rowR00567
4th rowR09573
5th rowR03446
ValueCountFrequency (%)
r08152 42
 
< 0.1%
r06728 39
 
< 0.1%
r09210 38
 
< 0.1%
r09908 37
 
< 0.1%
r09293 37
 
< 0.1%
r00739 37
 
< 0.1%
r04200 37
 
< 0.1%
r08475 36
 
< 0.1%
r03890 36
 
< 0.1%
r04109 36
 
< 0.1%
Other values (9990) 199625
99.8%
2025-05-20T19:56:38.136892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 279765
23.3%
R 200000
16.7%
5 80402
 
6.7%
9 80315
 
6.7%
3 80247
 
6.7%
1 80241
 
6.7%
6 80119
 
6.7%
4 79999
 
6.7%
2 79923
 
6.7%
8 79573
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 279765
23.3%
R 200000
16.7%
5 80402
 
6.7%
9 80315
 
6.7%
3 80247
 
6.7%
1 80241
 
6.7%
6 80119
 
6.7%
4 79999
 
6.7%
2 79923
 
6.7%
8 79573
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 279765
23.3%
R 200000
16.7%
5 80402
 
6.7%
9 80315
 
6.7%
3 80247
 
6.7%
1 80241
 
6.7%
6 80119
 
6.7%
4 79999
 
6.7%
2 79923
 
6.7%
8 79573
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 279765
23.3%
R 200000
16.7%
5 80402
 
6.7%
9 80315
 
6.7%
3 80247
 
6.7%
1 80241
 
6.7%
6 80119
 
6.7%
4 79999
 
6.7%
2 79923
 
6.7%
8 79573
 
6.6%
Distinct5000
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
2025-05-20T19:56:38.889036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1200000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD00315
2nd rowD03717
3rd rowD02035
4th rowD02657
5th rowD01026
ValueCountFrequency (%)
d03093 67
 
< 0.1%
d01999 66
 
< 0.1%
d01107 65
 
< 0.1%
d02137 65
 
< 0.1%
d00627 63
 
< 0.1%
d02429 63
 
< 0.1%
d04284 63
 
< 0.1%
d03342 62
 
< 0.1%
d03541 62
 
< 0.1%
d03941 62
 
< 0.1%
Other values (4990) 199362
99.7%
2025-05-20T19:56:39.768579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 299890
25.0%
D 200000
16.7%
3 100468
 
8.4%
1 100210
 
8.4%
2 99888
 
8.3%
4 99775
 
8.3%
8 60158
 
5.0%
5 60100
 
5.0%
6 59973
 
5.0%
9 59845
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 299890
25.0%
D 200000
16.7%
3 100468
 
8.4%
1 100210
 
8.4%
2 99888
 
8.3%
4 99775
 
8.3%
8 60158
 
5.0%
5 60100
 
5.0%
6 59973
 
5.0%
9 59845
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 299890
25.0%
D 200000
16.7%
3 100468
 
8.4%
1 100210
 
8.4%
2 99888
 
8.3%
4 99775
 
8.3%
8 60158
 
5.0%
5 60100
 
5.0%
6 59973
 
5.0%
9 59845
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 299890
25.0%
D 200000
16.7%
3 100468
 
8.4%
1 100210
 
8.4%
2 99888
 
8.3%
4 99775
 
8.3%
8 60158
 
5.0%
5 60100
 
5.0%
6 59973
 
5.0%
9 59845
 
5.0%

fare
Real number (ℝ)

Distinct4224
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.401285
Minimum2.97
Maximum82.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-20T19:56:39.993567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.97
5-th percentile8.12
Q111
median14.13
Q318.35
95-th percentile26.9905
Maximum82.74
Range79.77
Interquartile range (IQR)7.35

Descriptive statistics

Standard deviation6.163199
Coefficient of variation (CV)0.40017435
Kurtosis3.993813
Mean15.401285
Median Absolute Deviation (MAD)3.53
Skewness1.5093314
Sum3080256.9
Variance37.985022
MonotonicityNot monotonic
2025-05-20T19:56:40.218389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.97 196
 
0.1%
12.23 195
 
0.1%
12.48 194
 
0.1%
11.64 193
 
0.1%
11.03 193
 
0.1%
13.04 193
 
0.1%
13.42 193
 
0.1%
11.91 192
 
0.1%
11.3 191
 
0.1%
11.1 191
 
0.1%
Other values (4214) 198069
99.0%
ValueCountFrequency (%)
2.97 1
< 0.1%
3.5 1
< 0.1%
3.6 1
< 0.1%
3.74 1
< 0.1%
3.79 1
< 0.1%
3.83 1
< 0.1%
3.86 1
< 0.1%
3.89 1
< 0.1%
3.93 1
< 0.1%
3.95 1
< 0.1%
ValueCountFrequency (%)
82.74 1
< 0.1%
74.54 1
< 0.1%
72.64 1
< 0.1%
72.09 1
< 0.1%
71.32 1
< 0.1%
71.26 1
< 0.1%
70.28 1
< 0.1%
70.16 1
< 0.1%
69.15 1
< 0.1%
69.11 1
< 0.1%

surge_multiplier
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1414995
Minimum1
Maximum3.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-20T19:56:40.401966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.2
95-th percentile1.7
Maximum3.8
Range2.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.25536164
Coefficient of variation (CV)0.22370719
Kurtosis5.1322426
Mean1.1414995
Median Absolute Deviation (MAD)0
Skewness2.1464485
Sum228299.9
Variance0.065209568
MonotonicityNot monotonic
2025-05-20T19:56:40.594173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 132853
66.4%
1.2 10901
 
5.5%
1.3 10599
 
5.3%
1.1 10322
 
5.2%
1.4 9588
 
4.8%
1.5 7714
 
3.9%
1.6 5856
 
2.9%
1.7 4165
 
2.1%
1.8 2865
 
1.4%
1.9 1868
 
0.9%
Other values (17) 3269
 
1.6%
ValueCountFrequency (%)
1 132853
66.4%
1.1 10322
 
5.2%
1.2 10901
 
5.5%
1.3 10599
 
5.3%
1.4 9588
 
4.8%
1.5 7714
 
3.9%
1.6 5856
 
2.9%
1.7 4165
 
2.1%
1.8 2865
 
1.4%
1.9 1868
 
0.9%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.6 2
 
< 0.1%
3.5 2
 
< 0.1%
3.3 4
 
< 0.1%
3.2 3
 
< 0.1%
3.1 10
 
< 0.1%
3 11
 
< 0.1%
2.9 18
< 0.1%
2.8 21
< 0.1%
2.7 39
< 0.1%

tip
Real number (ℝ)

Zeros 

Distinct1088
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4695659
Minimum0
Maximum21.86
Zeros131307
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-20T19:56:41.079897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.4
95-th percentile2.61
Maximum21.86
Range21.86
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation1.100545
Coefficient of variation (CV)2.3437499
Kurtosis26.593001
Mean0.4695659
Median Absolute Deviation (MAD)0
Skewness4.1558573
Sum93913.18
Variance1.2111994
MonotonicityNot monotonic
2025-05-20T19:56:41.331061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 131307
65.7%
0.03 600
 
0.3%
0.01 586
 
0.3%
0.04 563
 
0.3%
0.07 550
 
0.3%
0.05 540
 
0.3%
0.08 536
 
0.3%
0.06 536
 
0.3%
0.02 519
 
0.3%
0.12 515
 
0.3%
Other values (1078) 63748
31.9%
ValueCountFrequency (%)
0 131307
65.7%
0.01 586
 
0.3%
0.02 519
 
0.3%
0.03 600
 
0.3%
0.04 563
 
0.3%
0.05 540
 
0.3%
0.06 536
 
0.3%
0.07 550
 
0.3%
0.08 536
 
0.3%
0.09 483
 
0.2%
ValueCountFrequency (%)
21.86 1
< 0.1%
19.13 1
< 0.1%
18.45 1
< 0.1%
17.94 2
< 0.1%
17.74 1
< 0.1%
16.88 1
< 0.1%
16.51 1
< 0.1%
16.45 1
< 0.1%
16.38 1
< 0.1%
16.28 1
< 0.1%

payment_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
Card
100326 
Mobile Money
79661 
Cash
20013 

Length

Max length12
Median length4
Mean length7.18644
Min length4

Characters and Unicode

Total characters1437288
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCard
2nd rowCard
3rd rowCard
4th rowMobile Money
5th rowCard

Common Values

ValueCountFrequency (%)
Card 100326
50.2%
Mobile Money 79661
39.8%
Cash 20013
 
10.0%

Length

2025-05-20T19:56:41.551318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T19:56:41.698764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
card 100326
35.9%
mobile 79661
28.5%
money 79661
28.5%
cash 20013
 
7.2%

Most occurring characters

ValueCountFrequency (%)
M 159322
11.1%
o 159322
11.1%
e 159322
11.1%
a 120339
8.4%
C 120339
8.4%
d 100326
 
7.0%
r 100326
 
7.0%
b 79661
 
5.5%
i 79661
 
5.5%
l 79661
 
5.5%
Other values (5) 279009
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1437288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 159322
11.1%
o 159322
11.1%
e 159322
11.1%
a 120339
8.4%
C 120339
8.4%
d 100326
 
7.0%
r 100326
 
7.0%
b 79661
 
5.5%
i 79661
 
5.5%
l 79661
 
5.5%
Other values (5) 279009
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1437288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 159322
11.1%
o 159322
11.1%
e 159322
11.1%
a 120339
8.4%
C 120339
8.4%
d 100326
 
7.0%
r 100326
 
7.0%
b 79661
 
5.5%
i 79661
 
5.5%
l 79661
 
5.5%
Other values (5) 279009
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1437288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 159322
11.1%
o 159322
11.1%
e 159322
11.1%
a 120339
8.4%
C 120339
8.4%
d 100326
 
7.0%
r 100326
 
7.0%
b 79661
 
5.5%
i 79661
 
5.5%
l 79661
 
5.5%
Other values (5) 279009
19.4%
Distinct199156
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size10.7 MiB
Minimum2024-04-27 00:07:34+02:27
Maximum2025-04-27 22:59:48+02:27
Invalid dates133295
Invalid dates (%)66.6%
2025-05-20T19:56:41.858591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:42.094227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct199152
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size10.7 MiB
Minimum2024-04-27 00:32:31+02:27
Maximum2025-04-27 23:18:51+02:27
Invalid dates133295
Invalid dates (%)66.6%
2025-05-20T19:56:42.318466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:42.548990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pickup_lat
Real number (ℝ)

High correlation 

Distinct199939
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8496
Minimum-1.7863602
Maximum30.544251
Zeros0
Zeros (%)0.0%
Negative66705
Negative (%)33.4%
Memory size1.5 MiB
2025-05-20T19:56:42.772773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7863602
5-th percentile-1.4458597
Q1-1.1726825
median6.5255743
Q329.934766
95-th percentile30.20387
Maximum30.544251
Range32.330611
Interquartile range (IQR)31.107448

Descriptive statistics

Standard deviation13.362151
Coefficient of variation (CV)1.1276458
Kurtosis-1.5200219
Mean11.8496
Median Absolute Deviation (MAD)7.8167464
Skewness0.51362791
Sum2369919.9
Variance178.54708
MonotonicityNot monotonic
2025-05-20T19:56:43.155758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.17032084 2
 
< 0.1%
30.02513751 2
 
< 0.1%
30.22747669 2
 
< 0.1%
29.94414941 2
 
< 0.1%
30.15494402 2
 
< 0.1%
6.4954843 2
 
< 0.1%
29.93779094 2
 
< 0.1%
-1.477307868 2
 
< 0.1%
30.06949062 2
 
< 0.1%
29.96275768 2
 
< 0.1%
Other values (199929) 199980
> 99.9%
ValueCountFrequency (%)
-1.78636024 1
< 0.1%
-1.786138458 1
< 0.1%
-1.786052555 1
< 0.1%
-1.786010688 1
< 0.1%
-1.786002169 1
< 0.1%
-1.785993166 1
< 0.1%
-1.785925265 1
< 0.1%
-1.785861207 1
< 0.1%
-1.785713235 1
< 0.1%
-1.785696613 1
< 0.1%
ValueCountFrequency (%)
30.54425122 1
< 0.1%
30.54420604 1
< 0.1%
30.54418911 1
< 0.1%
30.54409791 1
< 0.1%
30.54401692 1
< 0.1%
30.54396155 1
< 0.1%
30.54392746 1
< 0.1%
30.54391416 1
< 0.1%
30.5439123 1
< 0.1%
30.54386606 1
< 0.1%

pickup_lng
Real number (ℝ)

High correlation 

Distinct199909
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.924133
Minimum2.8792238
Maximum37.31709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-20T19:56:43.450035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.8792238
5-th percentile3.2192672
Q13.4965737
median31.238814
Q336.703772
95-th percentile36.976458
Maximum37.31709
Range34.437867
Interquartile range (IQR)33.207198

Descriptive statistics

Standard deviation14.577572
Coefficient of variation (CV)0.60932499
Kurtosis-1.4743753
Mean23.924133
Median Absolute Deviation (MAD)5.5736725
Skewness-0.64862442
Sum4784826.6
Variance212.5056
MonotonicityNot monotonic
2025-05-20T19:56:43.751869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.74522558 2
 
< 0.1%
36.88837441 2
 
< 0.1%
31.10638458 2
 
< 0.1%
31.3167754 2
 
< 0.1%
31.20898939 2
 
< 0.1%
36.85996114 2
 
< 0.1%
31.16173275 2
 
< 0.1%
36.95091208 2
 
< 0.1%
31.41844576 2
 
< 0.1%
31.30703943 2
 
< 0.1%
Other values (199899) 199980
> 99.9%
ValueCountFrequency (%)
2.879223832 1
< 0.1%
2.879417245 1
< 0.1%
2.879496497 1
< 0.1%
2.879615229 1
< 0.1%
2.879618582 1
< 0.1%
2.879640474 1
< 0.1%
2.879641904 1
< 0.1%
2.879654809 1
< 0.1%
2.879672369 1
< 0.1%
2.879694625 1
< 0.1%
ValueCountFrequency (%)
37.3170904 1
< 0.1%
37.31705642 1
< 0.1%
37.31698774 1
< 0.1%
37.31680759 1
< 0.1%
37.31666286 1
< 0.1%
37.31663847 1
< 0.1%
37.31660638 1
< 0.1%
37.31658794 1
< 0.1%
37.31657914 1
< 0.1%
37.31655886 1
< 0.1%

dropoff_lat
Real number (ℝ)

High correlation 

Distinct199958
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.849589
Minimum-1.8332202
Maximum30.592457
Zeros0
Zeros (%)0.0%
Negative66705
Negative (%)33.4%
Memory size1.5 MiB
2025-05-20T19:56:44.046184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.8332202
5-th percentile-1.4459386
Q1-1.1728684
median6.5252348
Q329.935056
95-th percentile30.204962
Maximum30.592457
Range32.425677
Interquartile range (IQR)31.107925

Descriptive statistics

Standard deviation13.362229
Coefficient of variation (CV)1.1276534
Kurtosis-1.5200087
Mean11.849589
Median Absolute Deviation (MAD)7.8165264
Skewness0.51362487
Sum2369917.7
Variance178.54916
MonotonicityNot monotonic
2025-05-20T19:56:44.270744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.11968501 2
 
< 0.1%
30.11683024 2
 
< 0.1%
29.87001763 2
 
< 0.1%
30.13205881 2
 
< 0.1%
30.2763164 2
 
< 0.1%
-1.24799864 2
 
< 0.1%
29.98393667 2
 
< 0.1%
29.92737718 2
 
< 0.1%
30.06795785 2
 
< 0.1%
6.71840666 2
 
< 0.1%
Other values (199948) 199980
> 99.9%
ValueCountFrequency (%)
-1.833220233 1
< 0.1%
-1.831403474 1
< 0.1%
-1.827552947 1
< 0.1%
-1.826719269 1
< 0.1%
-1.825446853 1
< 0.1%
-1.825320517 1
< 0.1%
-1.824689248 1
< 0.1%
-1.824483484 1
< 0.1%
-1.824437397 1
< 0.1%
-1.824145068 1
< 0.1%
ValueCountFrequency (%)
30.59245708 1
< 0.1%
30.59029799 1
< 0.1%
30.590092 1
< 0.1%
30.58756101 1
< 0.1%
30.58710143 1
< 0.1%
30.5863991 1
< 0.1%
30.58573358 1
< 0.1%
30.58404979 1
< 0.1%
30.58236108 1
< 0.1%
30.58117402 1
< 0.1%

dropoff_lng
Real number (ℝ)

High correlation 

Distinct199927
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.924173
Minimum2.8309785
Maximum37.364817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-20T19:56:44.482544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.8309785
5-th percentile3.2190504
Q13.4971949
median31.239118
Q336.704067
95-th percentile36.976494
Maximum37.364817
Range34.533838
Interquartile range (IQR)33.206872

Descriptive statistics

Standard deviation14.577642
Coefficient of variation (CV)0.60932689
Kurtosis-1.4743639
Mean23.924173
Median Absolute Deviation (MAD)5.5741678
Skewness-0.6486212
Sum4784834.7
Variance212.50765
MonotonicityNot monotonic
2025-05-20T19:56:44.994556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.28006589 2
 
< 0.1%
36.62873381 2
 
< 0.1%
3.29159534 2
 
< 0.1%
36.80034642 2
 
< 0.1%
31.20880007 2
 
< 0.1%
36.82506623 2
 
< 0.1%
36.92675393 2
 
< 0.1%
36.92982384 2
 
< 0.1%
31.30734986 2
 
< 0.1%
31.27091018 2
 
< 0.1%
Other values (199917) 199980
> 99.9%
ValueCountFrequency (%)
2.830978523 1
< 0.1%
2.831206114 1
< 0.1%
2.835444204 1
< 0.1%
2.836644356 1
< 0.1%
2.838114722 1
< 0.1%
2.838348575 1
< 0.1%
2.838472018 1
< 0.1%
2.838615336 1
< 0.1%
2.839658974 1
< 0.1%
2.839869603 1
< 0.1%
ValueCountFrequency (%)
37.3648167 1
< 0.1%
37.36304964 1
< 0.1%
37.36281314 1
< 0.1%
37.36063183 1
< 0.1%
37.35816552 1
< 0.1%
37.35758053 1
< 0.1%
37.35649716 1
< 0.1%
37.3557481 1
< 0.1%
37.35432949 1
< 0.1%
37.35371629 1
< 0.1%

weather
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
Sunny
120151 
Rainy
39976 
Cloudy
29874 
Foggy
 
9999

Length

Max length6
Median length5
Mean length5.14937
Min length5

Characters and Unicode

Total characters1029874
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFoggy
2nd rowSunny
3rd rowCloudy
4th rowCloudy
5th rowSunny

Common Values

ValueCountFrequency (%)
Sunny 120151
60.1%
Rainy 39976
 
20.0%
Cloudy 29874
 
14.9%
Foggy 9999
 
5.0%

Length

2025-05-20T19:56:45.217776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T19:56:45.342360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sunny 120151
60.1%
rainy 39976
 
20.0%
cloudy 29874
 
14.9%
foggy 9999
 
5.0%

Most occurring characters

ValueCountFrequency (%)
n 280278
27.2%
y 200000
19.4%
u 150025
14.6%
S 120151
11.7%
R 39976
 
3.9%
a 39976
 
3.9%
i 39976
 
3.9%
o 39873
 
3.9%
C 29874
 
2.9%
l 29874
 
2.9%
Other values (3) 59871
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1029874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 280278
27.2%
y 200000
19.4%
u 150025
14.6%
S 120151
11.7%
R 39976
 
3.9%
a 39976
 
3.9%
i 39976
 
3.9%
o 39873
 
3.9%
C 29874
 
2.9%
l 29874
 
2.9%
Other values (3) 59871
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1029874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 280278
27.2%
y 200000
19.4%
u 150025
14.6%
S 120151
11.7%
R 39976
 
3.9%
a 39976
 
3.9%
i 39976
 
3.9%
o 39873
 
3.9%
C 29874
 
2.9%
l 29874
 
2.9%
Other values (3) 59871
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1029874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 280278
27.2%
y 200000
19.4%
u 150025
14.6%
S 120151
11.7%
R 39976
 
3.9%
a 39976
 
3.9%
i 39976
 
3.9%
o 39873
 
3.9%
C 29874
 
2.9%
l 29874
 
2.9%
Other values (3) 59871
 
5.8%

city
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
Cairo
67436 
Nairobi
66705 
Lagos
65859 

Length

Max length7
Median length5
Mean length5.66705
Min length5

Characters and Unicode

Total characters1133410
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNairobi
2nd rowLagos
3rd rowNairobi
4th rowCairo
5th rowNairobi

Common Values

ValueCountFrequency (%)
Cairo 67436
33.7%
Nairobi 66705
33.4%
Lagos 65859
32.9%

Length

2025-05-20T19:56:45.508205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T19:56:45.648033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cairo 67436
33.7%
nairobi 66705
33.4%
lagos 65859
32.9%

Most occurring characters

ValueCountFrequency (%)
i 200846
17.7%
a 200000
17.6%
o 200000
17.6%
r 134141
11.8%
C 67436
 
5.9%
N 66705
 
5.9%
b 66705
 
5.9%
L 65859
 
5.8%
g 65859
 
5.8%
s 65859
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1133410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 200846
17.7%
a 200000
17.6%
o 200000
17.6%
r 134141
11.8%
C 67436
 
5.9%
N 66705
 
5.9%
b 66705
 
5.9%
L 65859
 
5.8%
g 65859
 
5.8%
s 65859
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1133410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 200846
17.7%
a 200000
17.6%
o 200000
17.6%
r 134141
11.8%
C 67436
 
5.9%
N 66705
 
5.9%
b 66705
 
5.9%
L 65859
 
5.8%
g 65859
 
5.8%
s 65859
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1133410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 200846
17.7%
a 200000
17.6%
o 200000
17.6%
r 134141
11.8%
C 67436
 
5.9%
N 66705
 
5.9%
b 66705
 
5.9%
L 65859
 
5.8%
g 65859
 
5.8%
s 65859
 
5.8%

loyalty_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
Bronze
121252 
Silver
48332 
Gold
21044 
Platinum
 
9372

Length

Max length8
Median length6
Mean length5.88328
Min length4

Characters and Unicode

Total characters1176656
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBronze
2nd rowGold
3rd rowBronze
4th rowBronze
5th rowGold

Common Values

ValueCountFrequency (%)
Bronze 121252
60.6%
Silver 48332
 
24.2%
Gold 21044
 
10.5%
Platinum 9372
 
4.7%

Length

2025-05-20T19:56:45.872317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T19:56:46.054411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bronze 121252
60.6%
silver 48332
 
24.2%
gold 21044
 
10.5%
platinum 9372
 
4.7%

Most occurring characters

ValueCountFrequency (%)
r 169584
14.4%
e 169584
14.4%
o 142296
12.1%
n 130624
11.1%
B 121252
10.3%
z 121252
10.3%
l 78748
6.7%
i 57704
 
4.9%
S 48332
 
4.1%
v 48332
 
4.1%
Other values (7) 88948
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1176656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 169584
14.4%
e 169584
14.4%
o 142296
12.1%
n 130624
11.1%
B 121252
10.3%
z 121252
10.3%
l 78748
6.7%
i 57704
 
4.9%
S 48332
 
4.1%
v 48332
 
4.1%
Other values (7) 88948
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1176656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 169584
14.4%
e 169584
14.4%
o 142296
12.1%
n 130624
11.1%
B 121252
10.3%
z 121252
10.3%
l 78748
6.7%
i 57704
 
4.9%
S 48332
 
4.1%
v 48332
 
4.1%
Other values (7) 88948
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1176656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 169584
14.4%
e 169584
14.4%
o 142296
12.1%
n 130624
11.1%
B 121252
10.3%
z 121252
10.3%
l 78748
6.7%
i 57704
 
4.9%
S 48332
 
4.1%
v 48332
 
4.1%
Other values (7) 88948
7.6%

Interactions

2025-05-20T19:56:30.291556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:19.125844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:21.199413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:23.062948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:24.442402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:25.979217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:28.083876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:30.499758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:19.395643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:21.593538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:23.261300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:24.658473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:26.243488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:28.507144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:30.701291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:19.608715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:21.868983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:23.444045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:24.889122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:26.557737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:29.051615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:30.886138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:19.823114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:22.087408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:23.653408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:25.092592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:26.950350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:29.262028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:31.141688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:20.039620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:22.398759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:23.864619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:25.284316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:27.213090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:29.560656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:31.351697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:20.746048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:22.639866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:24.065070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:25.514531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:27.506935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:29.811838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:31.559630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:20.952478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:22.859385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:24.254533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:25.752351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:27.789455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T19:56:30.074202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-20T19:56:46.203949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
citydropoff_latdropoff_lngfareloyalty_statuspayment_typepickup_latpickup_lngsurge_multipliertipweather
city1.0001.0001.0000.0040.0190.0001.0001.0000.0160.0060.000
dropoff_lat1.0001.000-0.4450.0030.0190.0000.998-0.4450.005-0.0020.000
dropoff_lng1.000-0.4451.000-0.0120.0190.000-0.4450.998-0.0250.0080.000
fare0.0040.003-0.0121.0000.0000.0030.003-0.0120.4530.0320.164
loyalty_status0.0190.0190.0190.0001.0000.0000.0190.0190.0000.0500.000
payment_type0.0000.0000.0000.0030.0001.0000.0000.0000.0000.0010.000
pickup_lat1.0000.998-0.4450.0030.0190.0001.000-0.4450.005-0.0020.000
pickup_lng1.000-0.4450.998-0.0120.0190.000-0.4451.000-0.0250.0080.000
surge_multiplier0.0160.005-0.0250.4530.0000.0000.005-0.0251.0000.0020.316
tip0.006-0.0020.0080.0320.0500.001-0.0020.0080.0021.0000.001
weather0.0000.0000.0000.1640.0000.0000.0000.0000.3160.0011.000

Missing values

2025-05-20T19:56:31.955300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-20T19:56:32.641557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trip_iduser_iddriver_idfaresurge_multipliertippayment_typepickup_timedropoff_timepickup_latpickup_lngdropoff_latdropoff_lngweathercityloyalty_status
0T000000R05207D0031512.111.00.00Card2024-11-27 18:41:50+02:272024-11-27 19:33:50+02:27-1.10812336.912209-1.06815536.875377FoggyNairobiBronze
1T000001R09453D037178.731.00.02Card2024-10-28 23:13:48+00:142024-10-28 23:26:48+00:146.6752663.5157406.6417343.525620SunnyLagosGold
2T000002R00567D0203519.681.00.00Card2025-02-17 05:36:41+02:272025-02-17 05:52:41+02:27-1.24858937.010668-1.27318237.018586CloudyNairobiBronze
3T000003R09573D0265716.431.00.01Mobile Money2024-06-18 19:27:14+02:052024-06-18 19:32:14+02:0529.81955431.18878029.83768931.232978CloudyCairoBronze
4T000004R03446D010268.701.01.06Card2024-10-05 09:58:16+02:272024-10-05 10:28:16+02:27-1.67647936.729219-1.63839536.694063SunnyNairobiGold
5T000005R05852D0484916.981.40.52Mobile Money2024-07-02 18:20:12+02:272024-07-02 18:52:12+02:27-1.37088037.041389-1.41039937.045473RainyNairobiSilver
6T000006R05611D0468617.491.40.00Card2024-10-19 07:17:45+02:052024-10-19 07:49:45+02:0529.90862431.09961729.88630531.127414RainyCairoSilver
7T000007R04890D0200625.621.70.00Mobile Money2024-12-06 17:23:11+02:272024-12-06 18:06:11+02:27-1.15377536.358126-1.13423636.392381RainyNairobiGold
8T000008R04341D0383514.971.00.00Card2024-09-15 20:25:57+00:142024-09-15 20:30:57+00:146.6957553.4091776.6791483.396333SunnyLagosSilver
9T000009R09904D0349412.151.30.11Mobile Money2025-03-19 08:53:00+02:052025-03-19 09:03:00+02:0529.84669931.27956429.85236331.260849SunnyCairoSilver
trip_iduser_iddriver_idfaresurge_multipliertippayment_typepickup_timedropoff_timepickup_latpickup_lngdropoff_latdropoff_lngweathercityloyalty_status
199990T199990R00705D031228.591.00.00Card2024-11-13 17:17:57+02:052024-11-13 18:10:57+02:0530.23767431.20688230.26586931.169655SunnyCairoBronze
199991T199991R00810D0301119.651.03.26Cash2024-06-13 23:14:54+02:272024-06-13 23:23:54+02:27-1.23420836.757649-1.25912036.768658SunnyNairobiBronze
199992T199992R02796D0255917.471.02.46Cash2025-01-04 06:16:29+02:272025-01-04 06:44:29+02:27-1.23891636.686793-1.28058036.734024SunnyNairobiBronze
199993T199993R09831D0282518.141.00.80Mobile Money2025-04-06 10:03:22+00:142025-04-06 10:18:22+00:146.4406633.5338936.4688723.566908SunnyLagosSilver
199994T199994R09903D016288.191.00.26Card2024-05-29 18:44:26+02:272024-05-29 19:31:26+02:27-1.20884436.876605-1.19250736.877234SunnyNairobiBronze
199995T199995R08022D0456226.791.30.00Card2025-03-14 18:16:26+00:142025-03-14 18:27:26+00:146.5113893.2971896.5455653.327685CloudyLagosSilver
199996T199996R05421D0398414.651.00.00Card2024-07-02 06:59:36+00:142024-07-02 07:36:36+00:146.4891433.4927866.4535813.514289SunnyLagosBronze
199997T199997R06619D0117312.871.20.00Mobile Money2024-05-06 20:38:57+00:142024-05-06 21:25:57+00:146.4593483.5276236.4519303.521616RainyLagosBronze
199998T199998R02867D0097417.181.30.00Mobile Money2024-09-25 03:11:33+00:142024-09-25 03:45:33+00:146.5400743.4716836.5403393.426481RainyLagosGold
199999T199999R07749D0489413.471.00.00Card2024-05-24 18:19:39+02:052024-05-24 18:51:39+02:0530.23427730.88400430.27922730.874865SunnyCairoBronze